Future of industry 5.0 in society: human-centric solutions, challenges and prospective research areas

Vision-based manipulation of transparent plastic bags in industrial setups

Introduction

This paper addresses the challenges of vision-based manipulation for autonomous cutting and unpacking of transparent plastic bags in industrial setups, contributing to the Industry 4.0 paradigm. Industry 4.0, emphasizing data-driven processes, connectivity, and robotics, enhances accessibility and sustainability across the value chain. Integrating autonomous systems, including collaborative robots (cobots), into industrial workflows is crucial for improving efficiency and safety.

Methods

The proposed system employs advanced Machine Learning algorithms, particularly Convolutional Neural Networks (CNNs), for identifying transparent plastic bags under diverse lighting and background conditions. Tracking algorithms and depth-sensing technologies are integrated to enable 3D spatial awareness during pick-and-place operations. The system incorporates vacuum gripping technology with compliance control for optimal grasping and manipulation points, using a Franka Emika robot arm.

Results

The system successfully demonstrates its capability to automate the unpacking and cutting of transparent plastic bags for an 8-stack bulk-loader. Rigorous lab testing showed high accuracy in bag detection and manipulation under varying environmental conditions, as well as reliable performance in handling and processing tasks. The approach effectively addressed challenges related to transparency, plastic bag manipulation and industrial automation.

Discussion

The results indicate that the proposed solution is highly effective for industrial applications requiring precision and adaptability, aligning with the principles of Industry 4.0. By combining advanced vision algorithms, depth sensing, and compliance control, the system offers a robust method for automating challenging tasks. The integration of cobots into such workflows demonstrates significant potential for enhancing efficiency, safety, and sustainability in industrial settings.

A systematic literature review on digital literacy, employability, and innovative work behavior: emphasizing the contextual approaches in HRM research

Knowledge Society 5.0 and Industry 5.0 require workers with adaptable employability skills and who engage in innovative work behavior that help companies to create innovative products and processes that are difficult for competitors to imitate. Extant research examines employability, and innovative work behavior, but there are still few articles that include digital literacy in their study. In fact, digital literacy is closely related to human resources in the new workforce whose daily activities are closely related to digital technology. Through bibliometric analysis and a systematic literature review of the interplay among digital literacy, employability, and innovative work behavior we synthesize research trends, measurements, theoretical frameworks, and conceptual models on these topics. In addition, some contextual considerations will be utilized to ensure accurate data interpretation. Findings suggest that there is no generic measure of digital literacy, especially in business contexts, that links this concept to either employability or innovative work behavior. Digital literacy is particularly important to increase employability and stimulate both innovative behavior and performance. Future research should explore these topics using various methodologies and theoretical frameworks, combining them with multiple perceptions across workers and countries, especially considering the pace of technological development.

Integrating collaborative robots in manufacturing, logistics, and agriculture: Expert perspectives on technical, safety, and human factors

This study investigates the implementation of collaborative robots across three distinct industrial sectors: vehicle assembly, warehouse logistics, and agricultural operations. Through the SESTOSENSO project, an EU-funded initiative, we examined expert perspectives on human-robot collaboration using a mixed-methods approach. Data were collected from 31 technical experts across nine European countries through an online questionnaire combining qualitative assessments of specific use cases and quantitative measures of attitudes, trust, and safety perceptions. Expert opinions across the use cases emphasized three primary concerns: technical impacts of cobot adoption, social and ethical considerations, and safety issues in design and deployment. In vehicle assembly, experts stressed the importance of effective collaboration between cobots and exoskeletons to predict and prevent collisions. For logistics, they highlighted the need for adaptable systems capable of handling various object sizes while maintaining worker safety. In agricultural settings, experts emphasized the importance of developing inherently safe applications that can operate effectively on uneven terrain while reducing workers' physical strain. Results reveal sector-specific challenges and opportunities: vehicle assembly operations require sophisticated sensor systems for cobot-exoskeleton integration; warehouse logistics demand advanced control systems for large object handling; and agricultural applications need robust navigation systems for uneven terrain. Quantitative findings indicate generally positive attitudes toward cobots, particularly regarding societal benefits, moderate to high levels of trust in cobot capabilities and favorable safety perceptions. The study highlights three key implications: (1) the need for comprehensive safety protocols tailored to each sector's unique requirements, (2) the importance of user-friendly interfaces and intuitive programming methods for successful cobot integration, and (3) the necessity of addressing workforce transition and skill development concerns. These findings contribute to our understanding of human-robot collaboration in industrial settings and provide practical guidance for organizations implementing collaborative robotics while considering both technological advancement and human-centered design principles.

Human-Centered Systems Thinking in Technology-Enhanced Sustainable and Inclusive Architectural Design

Human-centered systems thinking (HCST) can be seen as a promising enabler of effective Industry 5.0. This study primarily examined whether architecture students consider themselves systems thinkers and how this affects their design thinking, digital competency, and engagement in sustainable and inclusive design practices. Next, this study also examined the students’ HCST profiles, their stability, and the roles of digital competency, design thinking, motivation, and risk propensity in human-centered design. Using a person-oriented approach and cluster analysis, a sample of Polish architecture students from three universities (n = 208) was classified based on their self-perceived HCST ability. Three profiles were identified, namely high, average, and low HCST. A multivariate analysis of variance (MANOVA) revealed that the HCST profiles differed significantly in terms of design thinking and digital competencies, while multinomial logistic regression (MLR) analysis revealed that perceived intrinsic motivation predicted that students would be more likely to have a high HCST profile. MLR also revealed an undefined role of risk propensity in the context of HCST in inclusive and sustainable architecture design education. The findings indicate that it is essential to recognize and support students with low HCST throughout their education. It is also suggested to change the focus of architecture study programs to promote students’ systems thinking, and to encourage course designers to create novel and tailored technology-enhanced integrated human-centered design and systems thinking.

Prediction model of subacromial pain syndrome in assembly workers using shoulder range of motion and muscle strength based on support vector machine

Subacromial pain syndrome (SAPS) is the most common upper-extremity musculoskeletal problem among workers. In this study, a machine learning model was built to predict and classify the presence or absence of SAPS in assembly workers with shoulder joint range of motion (ROM) and muscle strength data using support vector machine (SVM). Permutation importance was used to determine important variables for predicting workers with or without SAPS. The accuracy of the support vector classifier (SVC) polynomial model for classifying workers with SAPS was 82.4%. The important variables in model construction were internal rotation and abduction of shoulder ROM and internal rotation of shoulder muscle strength. It is possible to accurately perform SAPS classification of workers with relatively easy-to-obtain shoulder ROM and muscle strength data using this model. In addition, preventing SAPS in workers is possible by adjusting the factors affecting model building using exercise or rehabilitation programs.Practitioner summary: This study aimed to create a machine learning model that can predict and classify SAPS using shoulder ROM and muscle strength and identify the variables that are of high importance in model construction. This model could be used to predict or classify workers' SAPS and manage or prevent SAPS.

Toward industry 5.0: Challenges and enablers of intelligent manufacturing technology implementation under the perspective of sustainability

The advancement of intelligent manufacturing technology in the era of Industry 5.0 has propelled the intelligence and automation of manufacturing production, while also exerting a significant impact on sustainable development of the manufacturing industry. However, the challenges and enablers faced by the transformation of intelligent manufacturing technology in the context of sustainable development of Industry 5.0 are still unclear. Based on literature review and expert opinions, this study uses the Likert scale to determine the challenges and enablers of the implementation of intelligent manufacturing technology in social, environmental and economic sustainability. The fuzzy-DEMETAL and AISM are used to analyze the logical relationship and hierarchical relationship between the above factors, and the MICMAC matrix is used to determine the key influencing factors. The research conclusions show that the most important challenges affecting the implementation of intelligent manufacturing technology are cost and funding, and the most important enabler is social benefits and public service improved. This research will provide insights for industry practitioners and decision makers in the management and decision-making process of implementing the transformation and upgrading of manufacturing intelligent manufacturing, thereby enhancing the sustainability of manufacturing development.

Requirements and Barriers for Human-Centered SMEs

With the advantages of new technologies and rising demand from customers, it is necessary to improve the manufacturing process. This necessity was recognized by the industry; therefore, the concept of Industry 4.0 has been implemented in various areas of manufacturing and services. The backbone and main aspect of Industry 4.0 is digitalization and the implementation of technologies into processes. While this concept helps manufacturers with the modernization and optimization of many attributes of the processes, Industry 5.0 takes a step further and brings importance to the human factor of industry practice, together with sustainability and resilience. The concept of Industry 5.0 contributes to the idea of creating a sustainable, prosperous, and human-friendly environment within companies. The main focus of the article is to analyze the existing literature regarding what is missing from the successful implementation of human centricity into industry practice, namely in small and medium-sized factories (SMEs). These findings are then presented in the form of requirements and barriers for the implementation of human centricity into SME factories, which can serve as guidelines for implementing human-centered manufacturing using axiomatic design theory in SMEs, which can serve as a roadmap for practitioners.

Exploring the application of ICTs in decarbonizing the agriculture supply chain: A literature review and research agenda

The contemporary agricultural supply chain necessitates the integration of information and communication technologies to effectively mitigate the multifaceted challenges posed by climate change and rising global demand for food products. Furthermore, recent developments in information and communication technologies, such as blockchain, big data analytics, the internet of things, artificial intelligence, cloud computing, etc., have made this transformation possible. Each of these technologies plays a particular role in enabling the agriculture supply chain ecosystem to be intelligent enough to handle today's world's challenges. Thus, this paper reviews the crucial information and communication technologies-enabled agriculture supply chains to understand their potential uses and contemporary developments. The review is supported by 57 research papers from the Scopus database. Five research areas analyze the applications of the technology reviewed in the agriculture supply chain: food safety and traceability, security and information system management, wasting food, supervision and tracking, agricultural businesses and decision-making, and other applications not explicitly related to the agriculture supply chain. The study also emphasizes how information and communication technologies can help agriculture supply chains and promote agriculture supply chain decarbonization. An information and communication technologies application framework for a decarbonized agriculture supply chain is suggested based on the research's findings. The framework identifies the contribution of information and communication technologies to decision-making in agriculture supply chains. The review also offers guidelines to academics, policymakers, and practitioners on managing agriculture supply chains successfully for enhanced agricultural productivity and decarbonization.

Defining human-centricity in Industry 5.0 and assessing the readiness of ergonomics/human factors communities in UK

There is a lack of a clear and consistent definition of human-centricity in Industry 5.0. This study identified the definition of human-centricity in Industry 5.0 through a systematic literature review and used it to assess the readiness of Ergonomics/Human Factors communities in the UK. The assessment of the communities readiness was conducted by reviewing UK accredited courses and events of three professional bodies; and interviewing practitioners (n = 8). Eleven themes were identified as elements of human-centricity from the thematic analysis of 30 publications. Gaps that had to be addressed to better equip UK practitioners to support the realisation of human-centricity in Industry 5.0 were also identified.

Regulatory and Ethical Considerations on Artificial Intelligence for Occupational Medicine

Generative artificial intelligence and Large Language Models are reshaping labor dynamics and occupational health practices. As AI continues to evolve, there's a critical need to customize ethical considerations for its specific impacts on occupational health. Recognizing potential ethical challenges and dilemmas, stakeholders and physicians are urged to proactively adjust the practice of occupational medicine in response to shifting ethical paradigms. By advocating for a comprehensive review of the International Commission on Occupational Health ICOH code of Ethics, we can ensure responsible medical AI deployment, safeguarding the well-being of workers amidst the transformative effects of automation in healthcare.

Towards a Human-Centric Digital Twin for Human-Machine Collaboration: A Review on Enabling Technologies and Methods

With the intent to further increase production efficiency while making human the centre of the processes, human-centric manufacturing focuses on concepts such as digital twins and human-machine collaboration. This paper presents enabling technologies and methods to facilitate the creation of human-centric applications powered by digital twins, also from the perspective of Industry 5.0. It analyses and reviews the state of relevant information resources about digital twins for human-machine applications with an emphasis on the human perspective, but also on their collaborated relationship and the possibilities of their applications. Finally, it presents the results of the review and expected future works of research in this area.

Enhancing worker-centred digitalisation in industrial environments: A KPI evaluation methodology

Recently, the European Commission announced Industry 5.0 as a strategic initiative toward a value-driven industrial transformation. This new paradigm coexists with previous Industry 4.0 revolution that has guided the efforts towards technology driven industrial digitalisation in the past ten years. As part of this Industry 4.0 strategies, numerous KPI-driven evaluation methods were proposed to cover the multiple pillars of smart industry assessment. However, they do not incorporate human workers and actors in a systematic way as drivers for digitalisation processes, as the new Industry 5.0 paradigm argues. This paper addresses this gap by proposing an evaluation methodology that incorporates multiple human actors in the digitalisation process. The final objective of this methodology is to evaluate the direct and indirect benefits of the technology-driven transformation process to achieve the goals of human workers and other human stakeholders. To this end, our methodology provides the basis for proposing assessment tools and instruments for technological and infrastructure integration, process optimisation, new functionalities and human factors benefits, and four core indicators that have been applied to a real case comparing the digitalisation processes of three different companies.

End-to-End Solution for Analog Gauge Monitoring Using Computer Vision in an IoT Platform

The emergence of Industry 4.0 and 5.0 technologies has enabled the digital transformation of various processes and the integration of sensors with the internet. Despite these strides, many industrial sectors still rely on visual inspection of physical processes, especially those employing analog gauges. This method of monitoring introduces the risk of human errors and inefficiencies. Automating these processes has the potential, not only to boost productivity for companies, but also potentially reduce risks for workers. Therefore, this paper proposes an end-to-end solution to digitize analog gauges and monitor them using computer vision through integrating them into an IoT architecture, to tackle these problems. Our prototype device has been designed to capture images of gauges and transmit them to a remote server, where computer vision algorithms analyze the images and obtain gauge readings. These algorithms achieved adequate robustness and accuracy for industrial environments, with an average relative error of 0.95%. In addition, the gauge data were seamlessly integrated into an IoT platform leveraging computer vision and cloud computing technologies. This integration empowers users to create custom dashboards for real-time gauge monitoring, while also enabling them to set thresholds, alarms, and warnings, as needed. The proposed solution was tested and validated in a real-world industrial scenario, demonstrating the solution's potential to be implemented in a large-scale setting to serve workers, reduce costs, and increase productivity.

Humans and robots: Friends of the future? A bird's eye view of biomanufacturing industry 5.0

The evolution of industries have introduced versatile technologies, motivating limitless possibilities of tackling pivotal global predicaments in the arenas of medicine, environment, defence, and national security. In this direction, ardently emerges the new era of Industry 5.0 through the eyes of biomanufacturing, which integrates the most advanced systems 21st century has to offer by means of integrating artificial systems to mimic and nativize the natural milieu to substitute the deficits of nature, thence leading to a new meta world. Albeit, it questions the natural order of the living world, which necessitates certain paramount stipulations to be addressed for a successful expansion of biomanufacturing Industry 5.0. Can humans live in synergism with artificial beings? How can humans establish dominance of hierarchy with artificial counterparts? This perspective provides a bird's eye view on the plausible direction of a new meta world inquisitively. For this purpose, we propose the influence of internet of things (IoT) via new generation interfacial systems, such as, human-machine interface (HMI) and brain-computer interface (BCI) in the domain of tissue engineering and regenerative medicine, which can be extended to target modern warfare and smart healthcare.

A Review of Optimization and Measurement Techniques of the Friction Stir Welding (FSW) Process

This review reports on the influencing parameters on the joining parts quality of tools and techniques applied for conducting process analysis and optimizing the friction stir welding process (FSW). The important FSW parameters affecting the joint quality are the rotational speed, tilt angle, traverse speed, axial force, and tool profile geometry. Data were collected corresponding to different processing materials and their process outcomes were analyzed using different experimental techniques. The optimization techniques were analyzed, highlighting their potential advantages and limitations. Process measurement techniques enable feedback collection during the process using sensors (force, torque, power, and temperature data) integrated with FSW machines. The use of signal processing coupled with artificial intelligence and machine learning algorithms produced better weld quality was discussed.

Can green finance facilitate Industry 5.0 transition to achieve sustainability? A systematic review with future research directions

Most of the world's rising carbon emission results from industrial activities. Previous industrial revolutions did not put much thought into safeguarding the natural world. Governments worldwide have been continuously implementing regulations and policies for the mitigation of climate change to promote sustainable development. To achieve decarbonization, the climate change discussion is merged with Industry 5.0 (I5.0) where green finance (GF) plays a crucial role. This technological metamorphosis of transition from Industry 4.0 (I4.0) to I5.0 will affect humans and their society. I5.0 forms a symbiotic relationship with different aspects of Society 5.0 (S5.0) such as social (human‒machine centricity), ecological (zero emissions), and technological (green innovations). Thus, the I5.0 transition prioritizes greening the economy in pursuit of achieving S5.0. Through a systematic review of 196 articles, this research study concisely summarizes the rapidly expanding body of information. The research domain gave six major themes: Green Innovations (GI), Green Manufacturing Practices (GMP), Circular Economy (CE), Green Supply Chain Management (GSCM), Emerging Economies, and Net Zero Economy (NZE). Finally, a framework has been provided that illustrates the supporting role of GF for the I5.0 transition eventually followed by S5.0. This study provides an overview of these themes with their propositions and future research directions. The present study addresses the knowledge gap by providing valuable contributions to the burgeoning research domain of I5.0 and GF. Moreover, it aims to garner the attention of different stakeholders to integrate these two concepts of research to attain the goal of sustainable development.

A Human Digital-Twin-Based Framework Driving Human Centricity towards Industry 5.0

This work presents a digital-twin-based framework focused on orchestrating human-centered processes toward Industry 5.0. By including workers and their digital replicas in the loop of the digital twin, the proposed framework extends the traditional model of the factory's digital twin, which instead does not adequately consider the human component. The overall goal of the authors is to provide a reference architecture to manufacturing companies for a digital-twin-based platform that promotes harmonization and orchestration between humans and (physical and virtual) machines through the monitoring, simulation, and optimization of their interactions. In addition, the platform enhances the interactions of the stakeholders with the digital twin, considering that the latter cannot always be fully autonomous, and it can require human intervention. The paper also presents an implemented scenario adhering to the proposed framework's specifications, which is also validated with a real case study set in a factory plant that produces wooden furniture, thus demonstrating the validity of the overall proposed approach.

Learning analytics for lifelong career development: a framework to support sustainable formative assessment and self-reflection in programs developing career self-efficacy

Among myriad complex challenges facing educational institutions in this era of a rapidly evolving job marketplace is the development of career self-efficacy among students. Self-efficacy has traditionally been understood to be developed through the direct experience of competence, the vicarious experience of competence, social persuasion, and physiological cues. These four factors, and particularly the first two, are difficult to build into education and training programs in a context where changing skills make the specific meaning of graduate competence largely unknown and, notwithstanding the other contributions in this collection, largely unknowable. In response, in this paper we argue for a working metacognitive model of career self-efficacy that will prepare students with the skills needed to evaluate their skills, attitudes and values and then adapt and develop them as their career context evolves around them. The model we will present is one of evolving complex sub-systems within an emergent milieu. In identifying various contributing factors, the model provides specific cognitive and affective constructs as important targets for actionable learning analytics for career development.

Hunting Network Anomalies in a Railway Axle Counter System

This paper presents a comprehensive investigation of machine learning-based intrusion detection methods to reveal cyber attacks in railway axle counting networks. In contrast to the state-of-the-art works, our experimental results are validated with testbed-based real-world axle counting components. Furthermore, we aimed to detect targeted attacks on axle counting systems, which have higher impacts than conventional network attacks. We present a comprehensive investigation of machine learning-based intrusion detection methods to reveal cyber attacks in railway axle counting networks. According to our findings, the proposed machine learning-based models were able to categorize six different network states (normal and under attack). The overall accuracy of the initial models was ca. 70-100% for the test data set in laboratory conditions. In operational conditions, the accuracy decreased to under 50%. To increase the accuracy, we introduce a novel input data-preprocessing method with the denoted gamma parameter. This increased the accuracy of the deep neural network model to 69.52% for six labels, 85.11% for five labels, and 92.02% for two labels. The gamma parameter also removed the dependence on the time series, enabled relevant classification of data in the real network, and increased the accuracy of the model in real operations. This parameter is influenced by simulated attacks and, thus, allows the classification of traffic into specified classes.

LSTM-Autoencoder for Vibration Anomaly Detection in Vertical Carousel Storage and Retrieval System (VCSRS)

Industry 5.0, also known as the "smart factory", is an evolution of manufacturing technology that utilizes advanced data analytics and machine learning techniques to optimize production processes. One key aspect of Industry 5.0 is using vibration data to monitor and detect anomalies in machinery and equipment. In the case of a vertical carousel storage and retrieval system (VCSRS), vibration data can be collected and analyzed to identify potential issues with the system's operation. A correlation coefficient model was used to detect anomalies accurately in the vertical carousel system to ascertain the optimal sensor placement position. This model utilized the Fisher information matrix (FIM) and effective independence (EFI) methods to optimize the sensor placement for maximum accuracy and reliability. An LSTM-autoencoder (long short-term memory) model was used for training and testing further to enhance the accuracy of the anomaly detection process. This machine-learning technique allowed for detecting patterns and trends in the vibration data that may not have been evident using traditional methods. The combination of the correlation coefficient model and the LSTM-autoencoder resulted in an accuracy rate of 97.70% for detecting anomalies in the vertical carousel system.

Is Industry 5.0 a Human-Centred Approach? A Systematic Review

Industry 5.0 presents itself as a strategy that puts the human factor at the centre of production, where the well-being of the worker is prioritized, as well as more sustainable and resilient production systems. For human centricity, it is necessary to empower human beings and, respectively, industrial operators, to improve their individual skills and competences in collaboration or cooperation with digital technologies. This research’s main purpose and distinguishing point are to determine whether Industry 5.0 is truly human-oriented and how human centricity can be created with Industry 5.0 technologies. For that, this systematic literature review article analyses and clarifies the concepts and ideologies of Industry 5.0 and its respective technologies (Artificial Intelligence, Robotics, Human-robot collaboration, Digitalization), as well as the strategies of human centricity, with the aim of achieving sustainable and resilient systems, especially for the worker.

Digital Citizenship: Beyond Big Data, Technical Skills, Industry 4.0, and COVID-19

Big data and data deluge are topics that are well known in the field of systems science. Digital transformation of big data and omics fields is also underway at present. These changes are impacting life sciences broadly, and high-throughput omics inquiries specifically. On the other hand, digital transformation also calls for rethinking citizenship and moving toward critically informed digital citizenship. Past approaches to digital citizenship have tended to frame the digital health issues narrowly, around technocracy, digital literacy, and technical competence in deployment and use of digital technologies. However, digital citizenship also calls for questioning the means and ends of digital transformation, the frames in which knowledge is produced in the current era. In this context, Industry 4.0 has been one of the innovation frameworks for automation through big data, and embedded sensors connected by wireless communication. Industry 4.0 and the attendant "smart" technologies relate to various automation approaches deployed as part of the public health responses to the COVID-19 pandemic as well. This article argues that there is a growing need to steer digital transformation toward critically informed digital citizenship, so that the provenance of digital data and knowledge is held to account from scientific design to implementation science, whether they concern academic or Industry 4.0 paradigms of innovation. There are enormous potentials and expectations from digital transformation in an era of COVID-19 and digital health. For this potential to materialize in ways that are efficient, democratic, and socially just, critical digital citizenship offers new ways forward. Systems science scholarship stands to benefit from a broadening of the focus on high-throughput omics technologies to a realm of critical digital citizenship, so the digital health innovations are well situated in their societal and political contexts.